Week 11

Linear regression (continued)

import seaborn as sns

df = sns.load_dataset('tips')
df

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

244 rows × 7 columns

Goal. Predict tips based on total bills using a function: $$f(\text{total bill}) = a\cdot (\text{total bill}) + b$$

• $x^{(i)}$ = the total of the customer $i$
• $y^{(i)}$ = the tip amount of the customer $i$

We want to find a function values of the parameters $a$ and $b$ such that the value

$$C(a, b) = \sum_i \left(y^{(i)} - f(x^{(i)})\right)^2$$

is as small as possible. Note that $x^{(i)}$ and $y^{(i)}$ are known (they are coming from the data), and the parameters $a, b$ are unknown. The function $C(a, b)$ is called the cost function.

From the course website:

from ipywidgets import interact, fixed
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

plt.style.use('ggplot')
sns.set_context("notebook")


df = sns.load_dataset('tips')

def tip_plot(a, b):
    
    predict = a*df['total_bill'] + b
    cost = ((predict - df['tip'])**2).sum()
    
    plt.figure(figsize=(12,7))
    sns.scatterplot(data=df, x="total_bill", y="tip", marker='o')
    x = np.arange(0, 55)
    plt.plot(x, a*x + b, c='b', lw=5, alpha=0.5)
    plt.ylim(0, 11)
    plt.title(f"a={a:.2f}   b={b:.2f}   C(a, b)={cost:.2f}", fontdict={'fontsize':20})
    plt.show()
    
interact(tip_plot, a=(0.1, 0.2, 0.01), b=(0.0, 1.0, 0.1));

interactive(children=(FloatSlider(value=0.15000000000000002, description='a', max=0.2, min=0.1, step=0.01), Fl…

Gradient descent

Gradient descent is a method of finding a minimum of a function $f$.

Algorithm:

1. Choose some starting point $\bf x$ and a number $\alpha > 0 $ (the learning rate).
2. Calculate the gradient $\nabla f(\bf x)$ of $f$ at the point $\bf x$. At the point $\bf x$ the function decreases fastest in the direction the vector $-\nabla f(\bf x)$.
3. Set the new value of $\bf x$ to be ${\bf x} -\alpha\nabla f(\bf x)$.
4. Repeat steps 1-3 some number of times.

From the course website:

import plotly.graph_objects as go
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


def descent(Df, x0, l_rate=0.1, nsteps=1000):
    '''
    Performs gradient descent of a given function f.

    Df:
        Differential of f
    x0:
        The xtarrting point.
    l_rate:
        The learning rate.
    nsteps:
        Number of iterations to run.

    Returns:
        A list of points computed during steps of the gradient descent.
    '''

    x = np.array(x0, dtype='float')
    path = [x]
    for i in range(nsteps):
        Dfx = np.array(Df(x))
        x = x - l_rate*Dfx
        path.append(x)
    return path


def plot_descent(f, xlim, ylim, path=None, levels=20):
    '''
    Creates contour plot of a functions and the path
    computed by gradient descent applied to the function.

    f:
        Function to be plotted
    path:
        List of coordinates of points computed by the
        gradient descent algorithm.
    xlim, ylim:
        Tuples with limits of x- and y-values for the contour
        plot of the function.
    levels:
        Specifies levels of the contour plot.
    '''

    plt.figure(figsize=(8, 8))
    x, y = np.meshgrid(np.linspace(*xlim, 1000), np.linspace(*ylim, 1000))
    Z = f(np.vstack([x.ravel(), y.ravel()])).reshape(x.shape)
    plt.contourf(x, y, Z, levels=levels, cmap='bone')
    plt.contour(x, y, Z, levels=levels, colors='gray')
    if path is not None:
        plt.plot([x[0] for x in path], [x[1] for x in path], 'ro-', ms=4)
    plt.show()


def plot_descent_step(f, xlim, ylim, path=None, levels=20, last=None, step=1):
    plot_descent(f=f,
                 xlim=xlim,
                 ylim=ylim,
                 path=path[:last:step],
                 levels=levels)


def plot3d(f, xlim, ylim):
    x = np.linspace(xlim[0], xlim[1], 400)
    y = np.linspace(ylim[0], ylim[1], 400)
    X, Y = np.meshgrid(x, y)
    Z = f(np.array([X, Y]))
    fig = go.Figure(go.Surface(x=X, y=Y, z=Z, colorscale="picnic"))
    fig.update_layout(autosize=False, width=800, height=600)
    fig.show()

Example 1. Gradient descent for $p(x, y) = ax^2 + by^2$.

a = 2
b = 1

def p(x):
    return a*x[0]**2 + b*x[1]**2

plot3d(p, (-5, 5), (-5, 5))

def Dp(x):
    return (2*a*x[0], 2*b*x[1])

path = descent(Dp, x0=[2,-3], l_rate=0.1, nsteps=50)

plot_descent(p,
             xlim=(-5, 5), 
             ylim=(-5, 5), 
             path=path,
             levels=40)

path[-1]

array([ 1.61656255e-11, -4.28174308e-05])

Note. This is a good example to illustrate why before using gradient descent to compute regression of data we need to normalize data features. In the function above if values of the parameters a and b are close to each other, gradient descent converges quickly. Convergence is much slower though if one of these parameters is much larger than the other:

a = 50
b = 1
plot3d(p, (-5, 5), (-5, 5))

a = 50
b = 1

path = descent(Dp, x0=[2,4], l_rate=0.015, nsteps=1000)

plot_descent(p,
             xlim=(-5, 5), 
             ylim=(-5, 5), 
             path=path,
             levels=40)

path[-1]

array([1.86652724e-301, 2.36479913e-013])

From the course website:

def h(x):
    '''
    Himmelblau's function
    h(x, y) = (x^2 + y - 11)^2 + (x + y^2 - 7)^2
    '''
    return (x[0]**2 + x[1] - 11)**2 + (x[0] + x[1]**2 - 7)**2

def Dh(x):
    return np.array([
        2 * (x[0]**2 + x[1] - 11) * 2 * x[0] + 2 * (x[0] + x[1]**2 - 7),
        2 * (x[0]**2 + x[1] - 11) + 2 * (x[0] + x[1]**2 - 7) * 2 * x[1]
    ])


def r(x):
    '''
    Rosenbrock function
    r(x, y) = (1-x)^2 + 100(y-x^2)^2
    '''
    return (1-x[0])**2 + 100*(x[1]-x[0]**2)**2

def Dr(x):
    return np.array([-2*(1-x[0]) - 400*(x[1]-x[0]**2)*x[0], 200*(x[1]-x[0]**2)])

Himmelblau function:

plot3d(h, (-5, 5), (-5, 5))

path = descent(Dh, x0=[-1,1], l_rate=0.01)
plot_descent(h, 
             xlim=(-5, 5), 
             ylim=(-5, 5), 
             path=path,
             levels=np.exp(np.linspace(-7, 6.5, 40)) - 2)

Rosenbrock function:

plot3d(r, (-1.4, 1.4), (-0.5, 1.4))

path = descent(Dr, x0=[-0.5,1], nsteps=20000, l_rate=0.001)
plot_descent(r, 
             xlim=(-1.4, 1.4), 
             ylim=(-0.5, 1.4), 
             path=path,
             levels= np.exp(np.linspace(-7, 6.5, 40)) - 2)

Linear regression with sklearn

from sklearn.linear_model import LinearRegression
reg = LinearRegression()

df

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

244 rows × 7 columns

reg.fit(df[['total_bill']], df['tip'])

LinearRegression()

reg.coef_, reg.intercept_

(array([0.10502452]), 0.9202696135546731)

plt.figure(figsize=(12,7))
sns.scatterplot(data=df, x="total_bill", y="tip", marker='o')
x = np.arange(0, 55)
plt.plot(x, reg.coef_*x + reg.intercept_, c='b')
plt.show()

reg.predict(df[['total_bill']])[:10]

array([2.70463616, 2.00622312, 3.12683472, 3.40725019, 3.5028225 ,
       3.57633966, 1.84133463, 3.74332864, 2.49983836, 2.47253198])

df['regression'] = reg.predict(df[['total_bill']])
df

	total_bill	tip	sex	smoker	day	time	size	regression
0	16.99	1.01	Female	No	Sun	Dinner	2	2.704636
1	10.34	1.66	Male	No	Sun	Dinner	3	2.006223
2	21.01	3.50	Male	No	Sun	Dinner	3	3.126835
3	23.68	3.31	Male	No	Sun	Dinner	2	3.407250
4	24.59	3.61	Female	No	Sun	Dinner	4	3.502822
...	...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3	3.969131
240	27.18	2.00	Female	Yes	Sat	Dinner	2	3.774836
241	22.67	2.00	Male	Yes	Sat	Dinner	2	3.301175
242	17.82	1.75	Male	No	Sat	Dinner	2	2.791807
243	18.78	3.00	Female	No	Thur	Dinner	2	2.892630

244 rows × 8 columns

df['regression_error'] = df['regression'] - df['tip']
df

	total_bill	tip	sex	smoker	day	time	size	regression	regression_error
0	16.99	1.01	Female	No	Sun	Dinner	2	2.704636	1.694636
1	10.34	1.66	Male	No	Sun	Dinner	3	2.006223	0.346223
2	21.01	3.50	Male	No	Sun	Dinner	3	3.126835	-0.373165
3	23.68	3.31	Male	No	Sun	Dinner	2	3.407250	0.097250
4	24.59	3.61	Female	No	Sun	Dinner	4	3.502822	-0.107178
...	...	...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3	3.969131	-1.950869
240	27.18	2.00	Female	Yes	Sat	Dinner	2	3.774836	1.774836
241	22.67	2.00	Male	Yes	Sat	Dinner	2	3.301175	1.301175
242	17.82	1.75	Male	No	Sat	Dinner	2	2.791807	1.041807
243	18.78	3.00	Female	No	Thur	Dinner	2	2.892630	-0.107370

244 rows × 9 columns

df[['regression_error']].describe()

	regression_error
count	2.440000e+02
mean	-5.223508e-16
std	1.019943e+00
min	-3.743435e+00
25%	-4.863111e-01
50%	9.744499e-02
75%	5.651615e-01
max	3.198225e+00

np.abs(df[['regression_error']]).describe()

	regression_error
count	244.000000
mean	0.745825
std	0.694074
min	0.002632
25%	0.276832
50%	0.541028
75%	0.999040
max	3.743435

Linear regression with total_bill and size

reg2 = LinearRegression()
reg2.fit(df[['total_bill', 'size']], df['tip'])
reg2.coef_, reg.intercept_

(array([0.09271334, 0.19259779]), 0.9202696135546731)

df['regression_2'] = reg2.predict(df[['total_bill', 'size']])
df

	total_bill	tip	sex	smoker	day	time	size	tip_fraction	naive_tip_prediction	naive_prediction_error	regression	regression_error	regression_2
0	16.99	1.01	Female	No	Sun	Dinner	2	0.059447	2.732036	1.722036	2.704636	1.694636	2.629340
1	10.34	1.66	Male	No	Sun	Dinner	3	0.160542	1.662699	0.002699	2.006223	0.346223	2.205394
2	21.01	3.50	Male	No	Sun	Dinner	3	0.166587	3.378462	-0.121538	3.126835	-0.373165	3.194645
3	23.68	3.31	Male	No	Sun	Dinner	2	0.139780	3.807805	0.497805	3.407250	0.097250	3.249592
4	24.59	3.61	Female	No	Sun	Dinner	4	0.146808	3.954135	0.344135	3.502822	-0.107178	3.719157
...	...	...	...	...	...	...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3	0.203927	4.668099	-1.251901	3.969131	-1.950869	3.938206
240	27.18	2.00	Female	Yes	Sat	Dinner	2	0.073584	4.370614	2.370614	3.774836	1.774836	3.574089
241	22.67	2.00	Male	Yes	Sat	Dinner	2	0.088222	3.645395	1.645395	3.301175	1.301175	3.155952
242	17.82	1.75	Male	No	Sat	Dinner	2	0.098204	2.865502	1.115502	2.791807	1.041807	2.706292
243	18.78	3.00	Female	No	Thur	Dinner	2	0.159744	3.019872	0.019872	2.892630	-0.107370	2.795297

244 rows × 13 columns

df['regression_2 error'] = df['regression_2'] - df['tip']
np.abs(df[['regression_error', 'regression_2 error']]).describe()

	regression_error	regression_2 error
count	244.000000	244.000000
mean	0.745825	0.739004
std	0.694074	0.685833
min	0.002632	0.002048
25%	0.276832	0.273011
50%	0.541028	0.536010
75%	0.999040	0.959264
max	3.743435	4.042497

Upshot: Adding size data does not seem to improve meaningfully the fit of the model.

Fit accuracy and $R^2$

reg.score(df[['total_bill']], df['tip'])

0.45661658635167657

reg2.score(df[['total_bill', 'size']], df['tip'])

0.46786930879612587

Linear regression with total_bill and day:

Create dummy variables for the day column:

dummies = pd.get_dummies(df['day'])
dummies

	Thur	Fri	Sat	Sun
0	0	0	0	1
1	0	0	0	1
2	0	0	0	1
3	0	0	0	1
4	0	0	0	1
...	...	...	...	...
239	0	0	1	0
240	0	0	1	0
241	0	0	1	0
242	0	0	1	0
243	1	0	0	0

244 rows × 4 columns

df = pd.concat([df, dummies], axis=1)
df

	total_bill	tip	sex	smoker	day	time	size	tip_fraction	naive_tip_prediction	naive_prediction_error	regression	regression_error	regression_2	regression_2 error	Thur	Fri	Sat	Sun
0	16.99	1.01	Female	No	Sun	Dinner	2	0.059447	2.732036	1.722036	2.704636	1.694636	2.629340	1.619340	0	0	0	1
1	10.34	1.66	Male	No	Sun	Dinner	3	0.160542	1.662699	0.002699	2.006223	0.346223	2.205394	0.545394	0	0	0	1
2	21.01	3.50	Male	No	Sun	Dinner	3	0.166587	3.378462	-0.121538	3.126835	-0.373165	3.194645	-0.305355	0	0	0	1
3	23.68	3.31	Male	No	Sun	Dinner	2	0.139780	3.807805	0.497805	3.407250	0.097250	3.249592	-0.060408	0	0	0	1
4	24.59	3.61	Female	No	Sun	Dinner	4	0.146808	3.954135	0.344135	3.502822	-0.107178	3.719157	0.109157	0	0	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3	0.203927	4.668099	-1.251901	3.969131	-1.950869	3.938206	-1.981794	0	0	1	0
240	27.18	2.00	Female	Yes	Sat	Dinner	2	0.073584	4.370614	2.370614	3.774836	1.774836	3.574089	1.574089	0	0	1	0
241	22.67	2.00	Male	Yes	Sat	Dinner	2	0.088222	3.645395	1.645395	3.301175	1.301175	3.155952	1.155952	0	0	1	0
242	17.82	1.75	Male	No	Sat	Dinner	2	0.098204	2.865502	1.115502	2.791807	1.041807	2.706292	0.956292	0	0	1	0
243	18.78	3.00	Female	No	Thur	Dinner	2	0.159744	3.019872	0.019872	2.892630	-0.107370	2.795297	-0.204703	1	0	0	0

244 rows × 18 columns

reg3 = LinearRegression()
reg3.fit(df[['total_bill', 'Thur', 'Fri', 'Sat', 'Sun']], df['tip'])
reg3.coef_, reg.intercept_

(array([ 0.1046728 , -0.01132963,  0.00755391, -0.07843209,  0.08220781]),
 0.9202696135546731)

df['regression_3'] = reg3.predict(df[['total_bill', 'Thur', 'Fri', 'Sat', 'Sun']])
df['regression_3 error'] = df['regression_3'] - df['tip']
np.abs(df[['regression_error', 'regression_3 error']]).describe()

	regression_error	regression_3 error
count	244.000000	244.000000
mean	0.745825	0.739488
std	0.694074	0.697760
min	0.002632	0.001820
25%	0.276832	0.252676
50%	0.541028	0.531382
75%	0.999040	1.010272
max	3.743435	3.828128

reg.score(df[['total_bill']], df['tip'])

0.45661658635167657

reg3.score(df[['total_bill', 'Thur', 'Fri', 'Sat', 'Sun']], df['tip'])

0.45887402603123395

Upshot: The day data does not improve the model.

Regression with k-NN

from sklearn.neighbors import KNeighborsRegressor

neigh = KNeighborsRegressor(n_neighbors=3)
neigh.fit(df[["total_bill"]], df["tip"])

KNeighborsRegressor(n_neighbors=3)

neigh.predict([[20]])

/Users/bb/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:450: UserWarning:

X does not have valid feature names, but KNeighborsRegressor was fitted with feature names

array([3.50666667])

String operations

s = "Hello there! How are you?"

s.lower()

'hello there! how are you?'

s.upper()

'HELLO THERE! HOW ARE YOU?'

s.split()

['Hello', 'there!', 'How', 'are', 'you?']

s.split("e")

['H', 'llo th', 'r', '! How ar', ' you?']

pieces = s.split()
pieces

['Hello', 'there!', 'How', 'are', 'you?']

"----".join(pieces)

'Hello----there!----How----are----you?'

s.find("there")

6

s[6:]

'there! How are you?'

s.replace("re", "<RE>")

'Hello the<RE>! How a<RE> you?'

Regular expressions

Raw strings

print("Hello\nthere\t!")

Hello
there	!

print("Hello\\nthere\\t!")

Hello\nthere\t!

print(r"Hello\nthere\t!")

Hello\nthere\t!

print("Hi \U0001F642")

Hi 🙂

print(r"Hi \U0001F642")

Hi \U0001F642

import re

From the course website:

sample = """I am writing in reference to the our conversation on Tuesday, March 2, 2021.
Your order of 100,000 units of GC-1344 microcontrollers has been confirmed.
You will be billed $19.50 per unit for the total of $1,950,000.00.
The expected delivery date is Friday, April 30, 2021. In case of any further
questions please contact our sales department by email support@simonis.biz,
phone (294) 934-0923 or fax (294) 934-0202.

Delfina Fischer
Manager, Simonis LLC
delfina.fisher@simonis.biz
(294) 934 0937
"""

import html
import re
from IPython.core.display import display, HTML


def re_show(regex, text="", flags=0):
    """
    Displays text with the regex match highlighted.
    """
    text_css = '''"border-style: none;
                   border-width: 0px;
                   padding: 0px;
                   font-size: 14px;
                   color: darkslategray;
                   background-color: white;
                   white-space: pre;
                   line-height: 20px;"
                   '''
    match_css = '''"padding: 0px 1px 0px 1px;
                    margin: 0px 0.5px 0px 0.5px;
                    border-style: solid;
                    border-width: 0.5px;
                    border-color: darkred;
                    background-color: cornsilk;
                    color: red;"
                    '''

    r = re.compile(f"({regex})", flags=flags)
    t = r.sub(fr'###START###\1###END###', text)
    t = html.escape(t)
    t = t.replace("###START###", f"<span style={match_css}>")
    t = t.replace("###END###", f"</span>")
    display(HTML(f'<code style={text_css}>{t}</code>'))

re_show(r"the", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

Alternative:

re_show(r"the|The", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

Grouping patterns:

re_show(r"T|the", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"(T|t)he", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

Character classes:

re_show(r"e.a", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"\w", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"\W", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

Note matches of newline characters:

sample

'I am writing in reference to the our conversation on Tuesday, March 2, 2021.\nYour order of 100,000 units of GC-1344 microcontrollers has been confirmed.\nYou will be billed $19.50 per unit for the total of $1,950,000.00.\nThe expected delivery date is Friday, April 30, 2021. In case of any further\nquestions please contact our sales department by email support@simonis.biz,\nphone (294) 934-0923 or fax (294) 934-0202.\n\nDelfina Fischer\nManager, Simonis LLC\ndelfina.fisher@simonis.biz\n(294) 934 0937\n'

re_show(r"\d", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"[A-Za-z]", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"[aeiouy]", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"[^A-Za-z]", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"a[a-z]", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"\d\d\d", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

Repetitions:

re_show(r"\d{3}", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"\d*", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"\d+", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"a\w?", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

Greedy vs non-greedy matching:

re_show(r"Y.*u", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"Y.*?u", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

Flags:

re_show(r"Y.*u", sample, flags=re.S)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"the", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show(r"the", sample, flags=re.I)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

Anchors:

re_show("^\w+", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show("\w+$", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show("^\w+", sample, flags=re.M)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show("\w+$", sample, flags=re.M)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re_show("^.*2021.*$", sample, re.M)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

Example. Match dollar amounts:

re_show(r"\$\d[\d,]*\.\d{2}", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

Example. Match phone numbers:

re_show(r"\(\d{3}\) \d{3}[ -]\d{4}", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

Example. Match emails:

re_show(r"[\w.]+@[\w.]+\w", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

Example. Match dates:

re_show(r"[A-Za-z]+, [A-Z-a-z]+ \d+, \d{4}", sample)

I am writing in reference to the our conversation on Tuesday, March 2, 2021. Your order of 100,000 units of GC-1344 microcontrollers has been confirmed. You will be billed $19.50 per unit for the total of $1,950,000.00. The expected delivery date is Friday, April 30, 2021. In case of any further questions please contact our sales department by email support@simonis.biz, phone (294) 934-0923 or fax (294) 934-0202. Delfina Fischer Manager, Simonis LLC delfina.fisher@simonis.biz (294) 934 0937

re.findall()

re.findall(r"\$\d[\d,]*\.\d{2}", sample)

['$19.50', '$1,950,000.00']

Match groups:

re.findall(r"\$(\d[\d,]*.\d{2})", sample)

['19.50', '1,950,000.00']

re.findall("fax [\d\(\) -]+", sample)

['fax (294) 934-0202']

re.findall("fax ([\d\(\) -]+)", sample)

['(294) 934-0202']

re.findall(r"\((\d{3})\) (\d{3})[ -](\d{4})", sample)

[('294', '934', '0923'), ('294', '934', '0202'), ('294', '934', '0937')]

Non-capturing match groups:

re.findall("(you|your|our) (\w+)", sample, flags=re.I)

[('our', 'conversation'), ('Your', 'order'), ('You', 'will'), ('our', 'sales')]

re.findall("(?:you|your|our) (\w+)", sample, flags=re.I)

['conversation', 'order', 'will', 'sales']

re.sub()

print(re.sub("\$\d[\d,]*\.\d{2}", "**AMOUNT**", sample))

I am writing in reference to the our conversation on Tuesday, March 2, 2021.
Your order of 100,000 units of GC-1344 microcontrollers has been confirmed.
You will be billed **AMOUNT** per unit for the total of **AMOUNT**.
The expected delivery date is Friday, April 30, 2021. In case of any further
questions please contact our sales department by email support@simonis.biz,
phone (294) 934-0923 or fax (294) 934-0202.

Delfina Fischer
Manager, Simonis LLC
delfina.fisher@simonis.biz
(294) 934 0937

re.findall(r"\((\d{3})\) (\d{3})[ -](\d{4})", sample)

[('294', '934', '0923'), ('294', '934', '0202'), ('294', '934', '0937')]

print(re.sub(r"\((\d{3})\) (\d{3})[ -](\d{4})", r"\1.\2.\3", sample))

I am writing in reference to the our conversation on Tuesday, March 2, 2021.
Your order of 100,000 units of GC-1344 microcontrollers has been confirmed.
You will be billed $19.50 per unit for the total of $1,950,000.00.
The expected delivery date is Friday, April 30, 2021. In case of any further
questions please contact our sales department by email support@simonis.biz,
phone 294.934.0923 or fax 294.934.0202.

Delfina Fischer
Manager, Simonis LLC
delfina.fisher@simonis.biz
294.934.0937

Regex exercises